As computers become more and more integrated into our modern society, there is a growing need for tools that will allow a user to efficiently enter data into a computer. Some of the most desired input tools are those that allow a user to enter data using natural input techniques rather than typing on a physical or software QWERTY keyboard. These natural input techniques, which include handwriting and speech, offer users the promise of a familiar and convenient method of creating data that requires no special skills. To this end, great strides have been made in developing applications that recognize text from such natural input. For example, some voice recognition applications will accurately transcribe text from a user's speech. Similarly, some handwriting recognition applications will recognize text from both block print and cursive handwriting.
One important advantage of these natural input techniques is that they potentially offer users a much higher throughput than entering data through a keyboard. The typical user can speak considerably faster than he or she can type. Thus, a user can quickly input text by speaking that would otherwise take much longer to input by typing on a keyboard. Likewise, if a user has not memorized the position of keys on a QWERTY keyboard, that user may be able to handwrite words with a stylus and a digitizer faster than he or she can type those words on a keyboard.
While natural input offers the potential for high throughput in theory, in actuality the inherent inaccuracies of natural input can significantly reduce that potential. For example, the voice recognition application may be unable to distinguish the spoken word “they're” from “there.” A handwriting recognition application may similarly be unable to distinguish the handwritten word “clog” from the handwritten word “dog.” Currently, natural input recognition engines provide recognition candidates based only upon the natural input object to be recognized. The destination or intended use of the recognized text has no influence over how a recognition engine selects a recognition candidate to recognize a natural input object.
In addition to the difficulties in accurately recognizing text from a natural input object, there are additional difficulties in correctly formatting the recognized text. For example, it is difficult for a natural input recognition engine to determine if recognized words should have spacing between them, and, if so how many spaces. Other than pauses, speech input provides no information as to spacing. While handwriting has some mechanisms for indicating spacing, such as proofing marks and leaving space between handwritten words, these cues are cumbersome for the user to employ or difficult for a recognition engine to accurately interpret. Similarly, it may be difficult for a natural input recognition engine to determine which characters in recognized text should be capitalized, if any. Still further, it may be difficult for a natural input recognition engine to properly recognize punctuation from a natural input object.
In an attempt to address this problem, some recognition engines include an automatic spacing feature that automatically provides spacing for recognized text. The solution has not worked very well, however. For example, if an automatic spacing process always inserts a space after recognized text, then when the user inserts a correction into an existing word, the automatic spacing process will create an unwanted space in the middle of the resulting word. On the other hand, when the user adds a word at a location where a space already exists, the automatic spacing process will create an extra, unwanted space.